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# SPDX-License-Identifier: Apache-2.0
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# Licensed under the Apache License, Version 2.0 (the "License");
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import os
import sys
import argparse
import numpy as np
import tensorrt as trt
from cuda import cudart
from image_batcher import ImageBatcher
from visualize import visualize_detections

sys.path.insert(1, os.path.join(os.path.dirname(os.path.realpath(__file__)), os.pardir))
import common


class TensorRTInfer:
    """
    Implements inference for the Model TensorRT engine.
    """

    def __init__(self, engine_path):
        """
        :param engine_path: The path to the serialized engine to load from disk.
        """

        # Load TRT engine
        self.logger = trt.Logger(trt.Logger.ERROR)
        trt.init_libnvinfer_plugins(self.logger, namespace="")
        with open(engine_path, "rb") as f, trt.Runtime(self.logger) as runtime:
            assert runtime
            self.engine = runtime.deserialize_cuda_engine(f.read())
        assert self.engine
        self.context = self.engine.create_execution_context()
        assert self.context

        # Setup I/O bindings
        self.inputs = []
        self.outputs = []
        self.allocations = []
        for i in range(self.engine.num_io_tensors):
            name = self.engine.get_tensor_name(i)
            is_input = False
            if self.engine.get_tensor_mode(name) == trt.TensorIOMode.INPUT:
                is_input = True
            dtype = self.engine.get_tensor_dtype(name)
            shape = self.engine.get_tensor_shape(name)
            if is_input:
                self.batch_size = shape[0]
            size = np.dtype(trt.nptype(dtype)).itemsize
            for s in shape:
                size *= s
            allocation = common.cuda_call(cudart.cudaMalloc(size))
            binding = {
                "index": i,
                "name": name,
                "dtype": np.dtype(trt.nptype(dtype)),
                "shape": list(shape),
                "allocation": allocation,
                "size": size,
            }
            self.allocations.append(allocation)
            if is_input:
                self.inputs.append(binding)
            else:
                self.outputs.append(binding)

        assert self.batch_size > 0
        assert len(self.inputs) > 0
        assert len(self.outputs) > 0
        assert len(self.allocations) > 0

    def input_spec(self):
        """
        Get the specs for the input tensor of the network. Useful to prepare memory allocations.
        :return: Two items, the shape of the input tensor and its (numpy) datatype.
        """
        return self.inputs[0]["shape"], self.inputs[0]["dtype"]

    def output_spec(self):
        """
        Get the specs for the output tensors of the network. Useful to prepare memory allocations.
        :return: A list with two items per element, the shape and (numpy) datatype of each output tensor.
        """
        specs = []
        for o in self.outputs:
            specs.append((o["shape"], o["dtype"]))
        return specs

    def infer(self, batch, scales=None, nms_threshold=None):
        """
        Execute inference on a batch of images. The images should already be batched and preprocessed, as prepared by
        the ImageBatcher class. Memory copying to and from the GPU device will be performed here.
        :param batch: A numpy array holding the image batch.
        :param scales: The image resize scales for each image in this batch. Default: No scale postprocessing applied.
        :return: A nested list for each image in the batch and each detection in the list.
        """

        # Prepare the output data.
        outputs = []
        for shape, dtype in self.output_spec():
            outputs.append(np.zeros(shape, dtype))

        # Process I/O and execute the network.
        common.memcpy_host_to_device(
            self.inputs[0]["allocation"], np.ascontiguousarray(batch)
        )

        self.context.execute_v2(self.allocations)
        for o in range(len(outputs)):
            common.memcpy_device_to_host(outputs[o], self.outputs[o]["allocation"])

        # Process the results.
        nums = outputs[0]
        boxes = outputs[1]
        scores = outputs[2]
        pred_classes = outputs[3]
        masks = outputs[4]

        detections = []
        for i in range(self.batch_size):
            detections.append([])
            for n in range(int(nums[i])):
                # Select a mask.
                mask = masks[i][n]

                # Calculate scaling values for bboxes.
                scale = self.inputs[0]["shape"][2]
                scale /= scales[i]
                scale_y = scale
                scale_x = scale

                if nms_threshold and scores[i][n] < nms_threshold:
                    continue
                # Append to detections
                detections[i].append(
                    {
                        "ymin": boxes[i][n][0] * scale_y,
                        "xmin": boxes[i][n][1] * scale_x,
                        "ymax": boxes[i][n][2] * scale_y,
                        "xmax": boxes[i][n][3] * scale_x,
                        "score": scores[i][n],
                        "class": int(pred_classes[i][n]),
                        "mask": mask,
                    }
                )
        return detections


def main(args):
    output_dir = os.path.realpath(args.output)
    os.makedirs(output_dir, exist_ok=True)

    labels = [
        "person",
        "bicycle",
        "car",
        "motorcycle",
        "airplane",
        "bus",
        "train",
        "truck",
        "boat",
        "traffic light",
        "fire hydrant",
        "stop sign",
        "parking meter",
        "bench",
        "bird",
        "cat",
        "dog",
        "horse",
        "sheep",
        "cow",
        "elephant",
        "bear",
        "zebra",
        "giraffe",
        "backpack",
        "umbrella",
        "handbag",
        "tie",
        "suitcase",
        "frisbee",
        "skis",
        "snowboard",
        "sports ball",
        "kite",
        "baseball bat",
        "baseball glove",
        "skateboard",
        "surfboard",
        "tennis racket",
        "bottle",
        "wine glass",
        "cup",
        "fork",
        "knife",
        "spoon",
        "bowl",
        "banana",
        "apple",
        "sandwich",
        "orange",
        "broccoli",
        "carrot",
        "hot dog",
        "pizza",
        "donut",
        "cake",
        "chair",
        "couch",
        "potted plant",
        "bed",
        "dining table",
        "toilet",
        "tv",
        "laptop",
        "mouse",
        "remote",
        "keyboard",
        "cell phone",
        "microwave",
        "oven",
        "toaster",
        "sink",
        "refrigerator",
        "book",
        "clock",
        "vase",
        "scissors",
        "teddy bear",
        "hair drier",
        "toothbrush",
    ]

    trt_infer = TensorRTInfer(args.engine)
    batcher = ImageBatcher(
        args.input, *trt_infer.input_spec(), config_file=args.det2_config
    )
    for batch, images, scales in batcher.get_batch():
        print(
            "Processing Image {} / {}".format(batcher.image_index, batcher.num_images),
            end="\r",
        )
        detections = trt_infer.infer(batch, scales, args.nms_threshold)
        for i in range(len(images)):
            basename = os.path.splitext(os.path.basename(images[i]))[0]
            # Image Visualizations
            output_path = os.path.join(output_dir, "{}.png".format(basename))
            visualize_detections(
                images[i], output_path, detections[i], labels, args.iou_threshold
            )
            # Text Results
            output_results = ""
            for d in detections[i]:
                line = [
                    d["xmin"],
                    d["ymin"],
                    d["xmax"],
                    d["ymax"],
                    d["score"],
                    d["class"],
                ]
                output_results += "\t".join([str(f) for f in line]) + "\n"
            with open(os.path.join(args.output, "{}.txt".format(basename)), "w") as f:
                f.write(output_results)
    print()
    print("Finished Processing")


if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument(
        "-e", "--engine", default=None, help="The serialized TensorRT engine"
    )
    parser.add_argument(
        "-i", "--input", default=None, help="Path to the image or directory to process"
    )
    parser.add_argument(
        "-c",
        "--det2_config",
        help="The Detectron 2 config file (.yaml) for the model",
        type=str,
    )
    parser.add_argument(
        "-o",
        "--output",
        default=None,
        help="Directory where to save the visualization results",
    )
    parser.add_argument(
        "-t",
        "--nms_threshold",
        type=float,
        help="Override the score threshold for the NMS operation, if higher than the threshold in the engine.",
    )
    parser.add_argument(
        "--iou_threshold",
        default=0.5,
        type=float,
        help="Select the IoU threshold for the mask segmentation. Range is 0 to 1. Pixel values more than threshold will become 1, less 0",
    )
    args = parser.parse_args()
    if not all([args.engine, args.input, args.output, args.det2_config]):
        parser.print_help()
        print(
            "\nThese arguments are required: --engine --input --output and --det2_config"
        )
        sys.exit(1)
    main(args)
